CSCI 3200: Programming Languages

1. CSCI 3200: Programming Languages Instructor: Dr. Erin Wolf Chambers Office: 301 Ritter Hall Email: echambe5@slu.edu

2. Today: • Syllabus overview (boring but necessary) • HW 1 is posted – due next Wednesday • An intro to programming languages

3. First Question: • What programming languages have you used before? • Python • C++/C • Java? • Matlab? • Others?

4. Categories of langauges • There are many ways to categorize programming languages. • Main starting point: High level versus low level • Examples? • In fact, initially, there only were extremely low level languages: each machine architecture had its own built in language.

5. High level languages • This began to change in the 1950s with Fortran, when people realized it would make more sense to have common languages and then translate them for the machine • This is the advent of the notion of compilation. • The idea was slow to catch on, since compiled code was usually slower to run.

6. Why so many? • Programming languages are still very much evolving: • Structured programming (using loops and function calls) was developed in the late 1960’s. • Object orientation was only introduced in the 1980’s. • Modern scripting languages (Ruby, Python,etc.) are often only 10-20 years old

7. Why so many? (cont) • Special purpose languages are very common: • C is good for low level coding, like OS development. • Prolog is good for logical relationships and AI applications. • Awk is good for character and string manipulations. • Python and perl are good for scripting.

8. Other issues: • Ease of use • Learning curve • Standardization • Open source • Good compliers available • Economics and hisotry • Pure inertia

9. Paradigms of computing • The major paradigms we’ll discuss this semester are: • Declarative languages: focus is on what the computer should do. • Imperative languages: focus is on how the computer should do something. (This is the dominant paradigm.)

10. Imperative language categories • von Neumann: Fortran, C, etc. - based on computation with variables • Scripting languages: bash, awk, perl, etc. - subset of von Neumann, but tailored for ease of expression over speed • Object oriented: - traces back to Simula 67, and descended from von Neumann, but focus is on objects rather than pure variables

11. Declarative language categories 1. Functional languages: Lisp, Scheme, Haskell, etc. • based on recursive definitions of functions • Inspired by lamba calculus 2. Logic based: prolog • computation is based on attempts to find values that satisfy specified relationships 3. Data flow: id, val • flow of information (tokens) among nodes

12. Some examples: • Consider the gcd algorithm (finding the greatest common divisor) • Euclid’s algorithm:

13. GCD in C int main() { inti = getint(), j = getint(); while (i != j) { if (i > j) i= i - j; else j = j - i; } putint(i); }

14. GCD in Haskell Haskell is based entirely on function calls – there is essentially no such thing as a variable in this language. selfGCD:: Integral f => f -> f -> f selfGCD a b = if b == 0 then a else selfGCD b (mod a b)

15. GCD in prolog • Prolog is all about stating true axioms, and then evaluating for something to be true based off these gcd(X,Y,Z):- X>=Y, X1=X-Y, gcd(X1,Y,Z). gcd(X,Y,Z):- X<Y, X1=Y- X, gcd(X1,X,Z). gcd(0,X,X):- X>0.

16. A note on outcomes(or: why the heck study this?) • Professionally, choosing an appropriate language is a key skill • Studying language design will make learning new languages easier • This also establishes a common terminology for comparison of languages • It is difficult to understand hidden “features” of various languages – we’ll look at a lot of them. • Need to understand actual implementation cost.

17. So: diving in • A first distinction is compilation versus interpretation • Compilation: • Interpretation:

18. Compilation vs. Interpretation • In reality, the difference is not so clear cut. • These are not opposites, and most languages fall somewhere in between on the spectrum • In general, interpretation gives greater flexibility (think python), but compilation gives better performance (think C++)

19. Compilation vs. Interpretation • Most languages do include a mix of these: • Note that compilation doesn’t have to produce machine code – just a translation to another language • Think of Java, for example

20. Implementation of compilation • Preprocessing: • removes white space and comments • groups characters into tokens • expands abbreviations • identifies higher level syntatic structures – i.e. loopsand subroutines • This is often known as scanning – we’ll spend the first few weeks talking about it.

21. Compiling (cont.) • Next: routines and linking • Compiler uses a linker program add subroutines from a library • You’ve done this if you ever used a #include from the standard template library

22. Compilers (cont) • Post-compilation assembly output: why?? • Makes debugging and optimizing easier, since assembler is MUCH easier than machine code • Isolates compiler from low level machine changes – many architectures can use the same assembly, but machine level code is very specific

23. Compilers (cont) • In interpreted languages, the compiler still generates code. • But assumptions about inputs are not finalized. • At runtime, checks assumptions. • If valid, runs quickly. • If not, a dynamic check reverts to the interpreter.

24. Phases of compilation

25. Next time • We’ll be spending our first few weeks on compilers, since a basic understanding of this helps to understand programming language design. • Remember, compilers is usually a class all by itself! We’ll be covering just enough of the basics to get us by. • We won’t even really get to the lower level stuff from the previous slide – go take a compilers course to cover that. • Next up: scanning and tokenizing